
MovingPandas offers functions to compute and/or visualize the speed of movement along the trajectory between consecutive points.
import pandas as pd
import geopandas as gpd
import movingpandas as mpd
import shapely as shp
import hvplot.pandas
from geopandas import GeoDataFrame, read_file
from shapely.geometry import Point, LineString, Polygon
from datetime import datetime, timedelta
from holoviews import opts
import warnings
warnings.filterwarnings('ignore')
opts.defaults(opts.Overlay(active_tools=['wheel_zoom'], frame_width=500, frame_height=400))
mpd.show_versions()
MovingPandas 0.10.rc1 SYSTEM INFO ----------- python : 3.9.13 | packaged by conda-forge | (main, May 27 2022, 16:50:36) [MSC v.1929 64 bit (AMD64)] executable : H:\miniconda3\envs\mpd-ex\python.exe machine : Windows-10-10.0.19043-SP0 GEOS, GDAL, PROJ INFO --------------------- GEOS : None GEOS lib : None GDAL : 3.5.0 GDAL data dir: None PROJ : 9.0.0 PROJ data dir: H:\miniconda3\pkgs\proj-9.0.0-h1cfcee9_1\Library\share\proj PYTHON DEPENDENCIES ------------------- geopandas : 0.10.2 pandas : 1.4.2 fiona : 1.8.21 numpy : 1.22.4 shapely : 1.8.2 rtree : 1.0.0 pyproj : 3.3.1 matplotlib : 3.5.2 mapclassify: 2.4.3 geopy : 2.2.0 holoviews : 1.14.9 hvplot : 0.8.0 geoviews : 1.9.5 stonesoup : 0.1b9
df = pd.DataFrame([
{'geometry':Point(0,0), 't':datetime(2018,1,1,12,0,0)},
{'geometry':Point(6,0), 't':datetime(2018,1,1,12,0,6)},
{'geometry':Point(6,6), 't':datetime(2018,1,1,12,0,11)},
{'geometry':Point(9,9), 't':datetime(2018,1,1,12,0,14)}
]).set_index('t')
gdf = GeoDataFrame(df, crs=31256)
toy_traj = mpd.Trajectory(gdf, 1)
toy_traj
Trajectory 1 (2018-01-01 12:00:00 to 2018-01-01 12:00:14) | Size: 4 | Length: 16.2m Bounds: (0.0, 0.0, 9.0, 9.0) LINESTRING (0 0, 6 0, 6 6, 9 9)
help(mpd.Trajectory.add_speed)
Help on function add_speed in module movingpandas.trajectory:
add_speed(self, overwrite=False, name='speed')
Add speed column and values to the trajectory's DataFrame.
Speed is calculated as CRS units per second, except if the CRS is
geographic (e.g. EPSG:4326 WGS84) then speed is calculated in meters
per second.
Parameters
----------
overwrite : bool
Whether to overwrite existing speed values (default: False)
name : str
Name of the speed column (default: "speed")
toy_traj.add_speed(overwrite=True)
toy_traj.df
| geometry | speed | |
|---|---|---|
| t | ||
| 2018-01-01 12:00:00 | POINT (0.000 0.000) | 1.000000 |
| 2018-01-01 12:00:06 | POINT (6.000 0.000) | 1.000000 |
| 2018-01-01 12:00:11 | POINT (6.000 6.000) | 1.200000 |
| 2018-01-01 12:00:14 | POINT (9.000 9.000) | 1.414214 |
We can also visualize the speed values:
toy_traj.plot(column="speed", linewidth=5, capstyle='round', legend=True)
<AxesSubplot:>
gdf = read_file('../data/geolife_small.gpkg')
traj_collection = mpd.TrajectoryCollection(gdf, 'trajectory_id', t='t')
my_traj = traj_collection.trajectories[1]
my_traj.df
| id | sequence | trajectory_id | tracker | geometry | |
|---|---|---|---|---|---|
| t | |||||
| 2009-06-29 07:02:25 | 1556 | 1090 | 2 | 0 | POINT (116.59096 40.07196) |
| 2009-06-29 07:02:30 | 1557 | 1091 | 2 | 0 | POINT (116.59091 40.07201) |
| 2009-06-29 07:02:35 | 1558 | 1092 | 2 | 0 | POINT (116.59088 40.07203) |
| 2009-06-29 07:02:40 | 1559 | 1093 | 2 | 0 | POINT (116.59091 40.07200) |
| 2009-06-29 07:02:45 | 1560 | 1094 | 2 | 0 | POINT (116.59096 40.07198) |
| ... | ... | ... | ... | ... | ... |
| 2009-06-29 11:09:47 | 2448 | 1982 | 2 | 0 | POINT (116.32349 40.00037) |
| 2009-06-29 11:09:57 | 2449 | 1983 | 2 | 0 | POINT (116.32513 40.00057) |
| 2009-06-29 11:10:02 | 2450 | 1984 | 2 | 0 | POINT (116.32688 40.00087) |
| 2009-06-29 11:10:07 | 2451 | 1985 | 2 | 0 | POINT (116.32722 40.00101) |
| 2009-06-29 11:13:12 | 2452 | 1986 | 2 | 0 | POINT (116.32746 40.00052) |
897 rows × 5 columns
Even if the GeoDataFrame does not contain a speed column, we can still plot movement speed:
my_traj.plot(column='speed', linewidth=5, capstyle='round', figsize=(9,3), legend=True, vmax=20)
<AxesSubplot:>
my_traj.hvplot(c='speed', clim=(0,20), line_width=7.0, tiles='CartoLight', cmap='Viridis', colorbar=True)
traj_collection.plot(column='speed', linewidth=5, capstyle='round', legend=True, vmax=20)
<AxesSubplot:>